Abstract:

A fault diagnosis method of vehicles driving cycle data acquisition devices were proposed based on a project sponsored by the China Automotive Test Cycle (CATC) and the Research on Monitoring Technology of Energy Saving and Consumption Reduction for Road Transportation Enterprises in Inner Mongolia (NJ-
2017-8) by using large data mining technology, a k-means clustering algorithm, an Apriori algorithm association rules, a correlation analysis algorithm, and made an empirical study with a case. The results show that the fault modes of data acquisition devices can be effectively analyzed by k-means algorithm, and the fault diagnosis and causes of data acquisition devices can effectively find out by Apriori algorithm of association rules and correlation between the characteristic parameters of data acquisition devices, which are consistent with the actual maintenance test results. Therefore, this method provides a reference for the operation, maintenance and control of data acquisition devices.

Key words: road traffic , vehicle running ,  big data mining , fault diagnosis , Apriori association algorithm , clustering algorithm , correlation analysis